This section provides background information related to the present technology which is not necessarily prior art.
In various biological processes, the specific binding of a target effector molecule to some ligand or small molecule drug compound results in some effect to biological activity. The binding might constitute part of a signaling mechanism between cells, it might be part of a mechanical operation such as muscle contraction, or it might mediate a catalytic event, or it might be part of yet another process. One way that drugs can work is competitive inhibition: binding to a target effector molecule more strongly than their natural binding partners, and thereby interrupting whatever process the target effector molecule mediates. Drugs can exert their effects using an allosteric mechanism, where they bind to an effector molecule and change its properties so that it can no longer perform native function(s).
When considering a target effector molecule and an arbitrary small organic molecule, it is useful to determine computationally, whether the small molecule will bind to the target effector molecule, and if so, it is useful to estimate the geometry of the bound complex, as well as the affinity of the binding. Most molecular binding algorithms include two components: a search technique to find the optimal placement of the ligand in the binding region of the target effector molecule, and a scoring function to rate each placement, as well as to rank candidate ligands against each other.
One of the critical questions for structural chemists and biologists is, how can a representative subset of the conformational ensemble typical of a given effector target molecule be obtained? Currently there exists only a limited set of means to generate static snap-shots of the three dimensional structure of macromolecules and there are no experimental methods for generating atomic movies and dynamic ensembles of structure with timescales sensitivity extending up to milliseconds. Static snap-shots of structure can be determined experimentally either from X-ray crystallography or NMR; dynamic ensembles can be generated via computational methods such as Monte Carlo (MC) or Molecular Dynamics (MD) simulations. Simulations typically use as a starting point a static snap-shot of the structure determined by one of the experimental methods. Ideally, the sampling used provides the most extensive coverage of the structure space. Comparisons done between traditional molecular simulations and experimental techniques seem to indicate that X-ray crystallography NMR structures seem to provide better coverage. Although experimental data is preferable, it is currently impossible to obtain the required amount of data needed to determine movies and dynamic ensembles at atomic resolution. A second critical question is, given a movie capturing comprising hundreds of thousands to millions of structures, what is the best way of combining this large amount of structural information for a docking study? This question also remains open. Current approaches use diverse ways of combining multiple structures.
As an exemplary target effector molecule, many non-coding RNAs perform their biological functions by undergoing large changes in conformation in response to specific cellular signals including the recognition of proteins, nucleic acids, metal ions, metabolites, vitamins, changes in temperature, and even RNA biosynthesis itself. J. M. Perez-Canadillas and G. Varani, Curr. Opin. Struct. Biol. 11 (1), 53 (2001). B. J. Tucker and R. R. Breaker, Curr Opin Struct Biol 15 (3), 342 (2005). E. Nudler, Cell 126 (1), 19 (2006). H. M. Al-Hashimi and N. G. Walter, Curr Opin Struct Biol In Press (2008). C. Musselman, H. M. Al-Hashimi, and I. Andricioaei, Biophys J 93 (2), 411 (2007). These conformational transitions guide RNA folding during co-transcriptional folding; provide the molecular basis for sensing and signaling transactions that allow riboswitches to regulate gene expression in response to changes in environmental conditions; allow ribozymes to dynamically meet the diverse structural requirements associated with their multi-step catalytic cycles; and enable complex ribonucleoproteins to assemble in a hierarchical and sequentially ordered manner.
Although it is clear that many non-coding RNAs (ncRNAs) undergo large changes in structure in order to carry out their function, the mechanism by which these conformational transitions occurs remains poorly understood. An intense area of investigation focuses on whether cellular factors such as proteins and ligands act catalytically to induce the RNA conformational change via ‘induced fit’, or they select and bind distinct RNA conformers from a pre-existing dynamical ensemble via ‘conformational selection’. Insights into such mechanistic questions have been impeded by lack of biophysical techniques that allow the 3D visualization of intrinsic RNA dynamics over biologically relevant timescales.
Recently, “conformational selection” from dynamical ensembles has emerged as a mechanism that rationalizes how different ligands bind different structures of the RNA receptor without necessitating either the “lock and key” or “induced fit” mechanisms. See Kumar et al. (1999) Cell Biochem. and Biophys. 31:141-164; Ma et al. (1999) Protein Eng. 12:713-720; Tsai et al. (1999) Protein Sci. 8:1181-1190. This mechanism assumes that macromolecules exist in solution as multiple, equilibrating conformations. These various conformations can be described by mechanical laws, using standard statistical distributions. The process of ligands binding to the receptors thus shifts the equilibrium from the statistical distribution of native conformations when the ligand is absent, to a new equilibrium that includes the receptor-ligand conformation. In this view, ligands bind to an ensemble of pre-existing receptor conformations. Ligand binding then shifts the overall dynamic equilibrium to stabilize the conformation present in the receptor-ligand complex.
This concept of conformationally mobile receptors (and ligands) is not new, but arose shortly after the discovery of modern conformational analysis. Previous thoughts on this topic posited that “the conformation of an enzyme in solution is regarded to be a statistical average of a number of conformations, the protein structure oscillating between these conformations.” Straub (1964) Advan. Enzymol. 26:89-114. Since then, the conformational mobility of biologically active proteins has been repeatedly demonstrated via biophysical methods.
Nevertheless, due to computational limitations, current molecular modeling and drug design efforts treat proteins and other biomolecules as static models even though they are clearly dynamic macromolecular structures, constantly in motion. In general, the static models portray either the native protein conformation or the protein conformation tightly bound to a potent peptide-derived inhibitor. Some modeling studies accommodate small changes in protein and ligand side chain conformations or hydrogen bonding interactions. This approach, called the “soft lock and key” model has subsequently been utilized to modify inhibitor design. Sowdhamini et al. (1995) Pharm. Acta Helv. 69:185-192.
But other protein conformations including ones that are significantly altered by motions that occur at timescales inaccessible to simulations are not considered when designing or modifying enzyme inhibitors, in spite of the fact that biophysical methods have established their existence, because their structural characteristics cannot in general be determined a priori with the required atomic resolution.
The atomic resolution characterization of dynamics in complex biomolecules is currently a major challenge in structural biology and biophysics. NMR spectroscopy is one of the most powerful techniques for characterizing dynamics uniquely providing comprehensive information regarding the amplitude, timescale and—in favorable cases—direction of motions with site-specific resolution. However, even with abundant measurements that can be made with the use of NMR, the total number of observables still pale in comparison to the total number of parameters needed to fully describe dynamics. MD simulations provide an all-atom description of dynamics; however, force fields remain to be thoroughly validated particularly for nucleic acids and simulation timescales remain limited to ˜100 nanoseconds (ns). Because they are complementary on the spatial and temporal scales, the limitations inherent to nuclear magnetic resonance (NMR) and MD could in principle be overcome by combining the two techniques; MD can fill the shortage in NMR data and NMR can provide a means for validating and potentially correcting force fields and accelerate MD conformational sampling to millisecond timescales.
There remains a long-felt and unmet need to resolve these dynamic conformations as a means to yield information that leads to the rational design of targeted, biologically-active compounds.
One of the goals of the present technology is to use NMR derived validation of target effector molecule structure and computer derived docking as a way to prioritize combinatorial library screening efforts.